Does Active Learning Contribute to Transfer Intent Among 2-Year College Students Beginning in STEM
Bibliographic Data
| ID | 9012162 |
|---|---|
| Authors | Xueli Wang (0009-0001-0033-636X, ,, corresponding author), Ning Sun (0009-0002-2479-0118, ,), Nian X Sun (0000-0002-3120-0094, Policy Analysis (United States)), Seo Young Lee (,), Brit Wagner (0000-0001-6828-4810, ,) |
| Year | 2017 |
| Volume | 88 |
| Issue | 4 |
| Pages | 593-618 |
| Publication date | 2017-03-10 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | The Journal of Higher Education (JOURNAL) |
| Journal identifiers | ISSN: 0022-1546 • E-ISSN: 1538-4640 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/00221546.2016.1272090 |
| OpenAlex | W2738319561 |
| Language | EN |
| Citations received | 7 |
| References cited | 50 |
This study explored whether and how beginning 2-year college students’ engagement in active learning within science, technology, engineering, and mathematics (STEM) classrooms is related to their intent to transfer to a 4-year institution. Despite the potentially important role active learning experiences play in shaping 2-year college students’ intent to transfer upward, there is a dearth of research to investigate this relationship. To fill this gap, we explored the linkage between active learning and intent to transfer. In addition, we explored whether and how transfer self-efficacy may mediate this relationship. Based on survey data collected from a statewide sample of 1st-year 2-year college students beginning in STEM programs or courses and controlling for student entry characteristics and postsecondary factors, a path analysis of mediation revealed that active learning is directly related to transfer intent and exerts an indirect relationship through its positive influence on transfer self-efficacy
Active learning (machine learning · Mathematics education · Medical education · Pedagogy · Artificial Intelligence · Career Development and Diversity · Computer Science · Innovative Teaching Methods · Medicine · Online and Blended Learning · Psychology
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| Unique citing works | 7 |
|---|---|
| Citations per year | 1,4 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 7 |